Fix issues

app.py

@@ -49,7 +49,7 @@ def read_markdown(path, parent="./sections/"):
 #     new_width = int(w * new_height / h)
 #     return cv2.resize(image, (new_width, new_height))
 
-checkpoints = ["./ckpt/ckpt-60k-5999"]  # TODO: Maybe add more checkpoints?
+checkpoints = ["./ckpt/vqa/ckpt-60k-5999"]  # TODO: Maybe add more checkpoints?
 dummy_data = pd.read_csv("dummy_vqa_multilingual.tsv", sep="\t")
 code_to_name = {
     "en": "English",

sections/intro.md

@@ -1,4 +1,4 @@
-This demo uses a [ViTBert model checkpoint](https://huggingface.co/flax-community/multilingual-vqa-pt-60k-ft/tree/main/ckpt-5999) fine-tuned on a [MarianMT](https://huggingface.co/transformers/model_doc/marian.html)-translated version of the [VQA v2 dataset](https://visualqa.org/challenge.html). The fine-tuning is performed afterpre-training using text-only Masked LM on approximately 10 million image-text pairs taken from the [Conceptual 12M dataset](https://github.com/google-research-datasets/conceptual-12m) translated using [MBart](https://huggingface.co/transformers/model_doc/mbart.html). The translations are performed in the following four languages: English, French, German and Spanish.
+This demo uses a [ViTBert model checkpoint](https://huggingface.co/flax-community/multilingual-vqa-pt-60k-ft/tree/main/ckpt-5999) fine-tuned on a [MarianMT](https://huggingface.co/transformers/model_doc/marian.html)-translated version of the [VQA v2 dataset](https://visualqa.org/challenge.html). The fine-tuning is performed after pre-training using text-only Masked LM on approximately 10 million image-text pairs taken from the [Conceptual 12M dataset](https://github.com/google-research-datasets/conceptual-12m) translated using [MBart](https://huggingface.co/transformers/model_doc/mbart.html). The translations are performed in the following four languages: English, French, German and Spanish.
 
 The model predicts one out of 3129 classes in English which can be found [here](https://huggingface.co/spaces/flax-community/Multilingual-VQA/blob/main/answer_reverse_mapping.json), and then the translated versions are provided based on the language chosen as `Answer Language`. The question can be present or written in any of the following: English, French, German and Spanish.